Transitioning from global to local computational strategies during brain-machine interface learning

Abstract

When learning to use a brain-machine interface (BMI), the brain modulates neuronal activity patterns, exploring and exploiting the state space defined by their neural manifold. Neurons directly involved in BMI control can display marked changes in their firing patterns during BMI learning. However, whether these changes extend to neurons not directly involved in BMI control remains unclear. To clarify this issue, we studied BMI learning in animals that were required to control the position of a platform with their neural signals. Animals that learned to control the platform and improved their performance in the task shifted from a global strategy, where both direct and indirect neurons modified their firing patterns, to a local strategy, where only direct neurons modified their firing rate, as animals became expert in the task. These results provide important insights into what differentiates successful and unsuccessful BMI learning and the computational mechanisms adopted by the neurons.